CODE 90539 ACADEMIC YEAR 2019/2020 CREDITS 6 cfu anno 1 COMPUTER SCIENCE 10852 (LM-18) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR INF/01 LANGUAGE English TEACHING LOCATION GENOVA SEMESTER 2° Semester TEACHING MATERIALS AULAWEB OVERVIEW The course offers an introduction to state-of-the-art methods for visual data analysis. In particular it deals with image and video understanding. AIMS AND CONTENT LEARNING OUTCOMES Learning how to represent image content adaptively by means of shallow or deep computational models and biologically-inspired hierarchical models, and how to tackle image classification and categorization problems. AIMS AND LEARNING OUTCOMES Students will be provided with an an overview of state-of-the-art methods for modeling and understanding the semantics of a scene. Students will get acquainted with the problem of representing the image content adaptively by means of shallow or deep computational models. Then it will address image classification and categorization problems. Possible extensions to depth and motion information will also be discussed. Students will be involved in project activities. PREREQUISITES Calculus and linear algebra. Digital image processing and machine learning principles. TEACHING METHODS Theoretical classes complemented by practical activities SYLLABUS/CONTENT Course content Introductory classes Reviewing background knowledge from image processing (filters, features, histograms, color, ...) and machine learning (clustering and classification algorithms) Problems formulation: image matching, image retrieval, image classification Image representations Early approaches: keypoints and bag-of-keypoints Sparse coding over fixed over-complete dictionaries Learning adaptive dictionaries (dictionary learning) Coding-pooling approaches Deep architectures Additional topics: using context, dealing with temporal or depth information, data visualization issues Projects and study cases RECOMMENDED READING/BIBLIOGRAPHY material provided by the instructors (slides and papers) additional reference online book http://szeliski.org/Book/ TEACHERS AND EXAM BOARD FRANCESCA ODONE Ricevimento: Appointment by email: francesca.odone@unige.it (always specify name and surname, course name, degree name) Exam Board FRANCESCA ODONE (President) LORENZO ROSASCO (President) ANNALISA BARLA NICOLETTA NOCETI ALESSANDRO VERRI LESSONS Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION 50% theory (oral exam) 50% application (individual project+seminar) ASSESSMENT METHODS timely delivery of assignments active participation in class and on the online students forum (aulaweb) final project on a use-case (datathon-like) and presentation of the obtained results in a seminar oral exam Exam schedule Data appello Orario Luogo Degree type Note 13/02/2020 09:00 GENOVA Esame su appuntamento 03/07/2020 09:00 GENOVA Scritto 23/07/2020 09:00 GENOVA Esame su appuntamento 24/07/2020 09:00 GENOVA Esame su appuntamento 17/09/2020 09:00 GENOVA Esame su appuntamento 18/09/2020 09:00 GENOVA Esame su appuntamento 11/02/2021 09:00 GENOVA Esame su appuntamento 12/02/2021 09:00 GENOVA Esame su appuntamento